# RTMDx Analysis: West Ward Agg Assault Jan-Jun Risk Terrain Modeling (RTM) identifies how certain qualities of geographic space interact and overlap to influence behaviors and outcomes pertaining to your study topic: undefined. This diagnosis of spatial vulnerabilities throughout the study area is used for forecasting, resource deployments, risk mitigation, problem solving, and other highly actionable decision-making efforts ## RTM Results MAPS: Risk terrain maps show vulnerable places within the study area. These are places where the spatial influence of risk factors in the risk terrain model (see "RTM Results TABLE") exist with varying levels of intensity. Highly vulnerable places are more likely to experience future events pertaining to your study topic than elsewhere. Consider allocating resources accordingly. A Relative Risk Score (RRS) was assigned to each place in your study area, ranging from 1 for the lowest risk to 89.009 for the highest risk place. These scores allow for easy comparison among places in the risk terrain map. For instance, a place with an RRS of 10 has an expected rate of events pertaining to your study topic that is 10 times higher than a place with a score of 1. Analysis units of forecast = 400 ft places Total number of places: 2130 ## RRS Statistics: Range = 1 to 89.009 Mean = 4.197; Standard Deviation = 6.131 Number of Places greater than 2 Standard Deviations from the Mean: 57 (0.43% of the study area) ## RTM Results TABLE: Understand risk factors in the risk terrain model according to the operationalization, spatial influence, and relative risk value (RRV). Interpret RRVs as risk factor weights; places affected by a risk factor with a RRV of 6 are twice as risky compared to places affected by risk factor with a RRV of 3. Develop risk narratives for the study topic based on the RTM Results Table. You may choose to prioritize the risk factors for mitigation based on the RRVs and/or your risk narratives. RRV, Risk Factor, Operationalization, Spatial Influence 24.066, RF - Train_Stations.shp, Proximity, 800 3.699, RF - LG_Parking_Lot.shp, Proximity, 400 3.267, RF - Clinics.shp, Density, 1200 2.415, Abandoned_Buildings2019 - Abandoned_Buildings2019.shp, Proximity, 400 ## Summary of Analysis Paremeters: Boundary File: BM - Newark_Wards.shp Study Area Name: South Ward Topic Issue Data File: Crime_Thru_6_30_M-F - Crime_Thru_6_30_M-F.shp Topic Issue Name: Agg Assault Standard Value: 400 ft Place Size: 200 ft ## Filters Study Area Filtered to Subarea Polygon ("WARD_NAME"): WEST By Time of Day ("STARTTIME"): 7:00 AM - 5:00 PM By Value ("INC_TYPE_P"): Agg Assault ## All Risk Factors Tested: Risk Factor, Operationalization, SVM, Increments Newark_CoffeeShops, Proximity or Density, 3, Whole Newark_Clubs, Proximity or Density, 3, Whole Newark_Laundromats, Proximity or Density, 3, Whole RF - ATMs.shp, Proximity or Density, 3, Whole RF - AtRisk_Housing.shp, Proximity or Density, 3, Whole RF - Banks.shp, Proximity or Density, 3, Whole RF - Bars.shp, Proximity or Density, 3, Whole RF - CheckCashingSvcs.shp, Proximity or Density, 3, Whole RF - Clinics.shp, Proximity or Density, 3, Whole RF - ConvenienceStores.shp, Proximity or Density, 3, Whole RF - GasStations.shp, Proximity or Density, 3, Whole RF - GroceryStores.shp, Proximity or Density, 3, Whole RF - Hospitals.shp, Proximity or Density, 3, Whole RF - Jewelry_Store.shp, Proximity or Density, 3, Whole RF - LG_School.shp, Proximity or Density, 3, Whole RF - LG_Parking_Lot.shp, Proximity or Density, 3, Whole RF - Liquor_Store.shp, Proximity or Density, 3, Whole RF - Parks.shp, Proximity or Density, 3, Whole RF - Pawn_Shops.shp, Proximity or Density, 3, Whole RF - Pharmacies.shp, Proximity or Density, 3, Whole RF - Restaurant_w_Liquor.shp, Proximity or Density, 3, Whole RF - Restaurants.shp, Proximity or Density, 3, Whole RF - Scrap_Metal.shp, Proximity or Density, 3, Whole RF - Train_Stations.shp, Proximity or Density, 3, Whole Vacant_Lots - Vacant_Lots.shp, Proximity or Density, 3, Whole RF - Parks Updated, Proximity or Density, 3, Whole Abandoned_Buildings2019 - Abandoned_Buildings2019.shp, Proximity or Density, 3, Whole R Script Results: ==== Family: c("PO", "Poisson") Call: gamlss(formula = crime_count ~ r16p02_RF_._LG_Parking_Lot.shp_proximity_400 + r26p03_RF_._Train_Stations.shp_proximity_800 + r10d01_RF_._Clinics.shp_density_1200 + r01p02_Abandoned_Buildings2019_._Abandoned_Buildings2019.shp_proximity_400, sigma.formula = ~1, family = PO, data = raster.data, method = mixed(3, 10)) Fitting method: mixed(3, 10) ------------------------------------------------------------------- Mu link function: log Mu Coefficients: Estimate Std. Error (Intercept) -5.2916 0.3436 r16p02_RF_._LG_Parking_Lot.shp_proximity_400 1.3080 0.3321 r26p03_RF_._Train_Stations.shp_proximity_800 3.1808 0.5615 r10d01_RF_._Clinics.shp_density_1200 1.1838 0.3325 r01p02_Abandoned_Buildings2019_._Abandoned_Buildings2019.shp_proximity_400 0.8818 0.3044 t value Pr(>|t|) (Intercept) -15.402 7.937e-51 r16p02_RF_._LG_Parking_Lot.shp_proximity_400 3.938 8.477e-05 r26p03_RF_._Train_Stations.shp_proximity_800 5.665 1.668e-08 r10d01_RF_._Clinics.shp_density_1200 3.560 3.786e-04 r01p02_Abandoned_Buildings2019_._Abandoned_Buildings2019.shp_proximity_400 2.897 3.808e-03 ------------------------------------------------------------------- No. of observations in the fit: 2130 Degrees of Freedom for the fit: 5 Residual Deg. of Freedom: 2125 at cycle: 1 Global Deviance: 391.1564 AIC: 401.1564 SBC: 429.4758